Findings about LORETA Applied to High-Density EEG—A Review

Dattola, Serena; Morabito, Francesco Carlo; Mammone, Nadia; Foresta, Fabio La

doi:10.3390/electronics9040660

Cited by 22 publications

(16 citation statements)

References 64 publications

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“…A mathematical approach for representing current source density of EEG recordings in 3D space is eLORETA, which we also employed in the current study. Several studies have confirmed that eLORETA has zero localization error in the presence of measurement and structured biological noise ( Dattola et al, 2020 ). It should be noted though that eLORETA relies on a standard head model that does not account for interindividual variability in brain size and shape as well as tissue conductivity that can affect localization accuracy.…”

Section: Discussionmentioning

confidence: 92%

Head-Down Tilt Position, but Not the Duration of Bed Rest Affects Resting State Electrocortical Activity

et al. 2021

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Adverse cognitive and behavioral conditions and psychiatric disorders are considered a critical and unmitigated risk during future long-duration space missions (LDSM). Monitoring and mitigating crew health and performance risks during these missions will require tools and technologies that allow to reliably assess cognitive performance and mental well-being. Electroencephalography (EEG) has the potential to meet the technical requirements for the non-invasive and objective monitoring of neurobehavioral conditions during LDSM. Weightlessness is associated with fluid and brain shifts, and these effects could potentially challenge the interpretation of resting state EEG recordings. Head-down tilt bed rest (HDBR) provides a unique spaceflight analog to study these effects on Earth. Here, we present data from two long-duration HDBR experiments, which were used to systematically investigate the time course of resting state electrocortical activity during prolonged HDBR. EEG spectral power significantly reduced within the delta, theta, alpha, and beta frequency bands. Likewise, EEG source localization revealed significantly lower activity in a broad range of centroparietal and occipital areas within the alpha and beta frequency domains. These changes were observed shortly after the onset of HDBR, did not change throughout HDBR, and returned to baseline after the cessation of bed rest. EEG resting state functional connectivity was not affected by HDBR. The results provide evidence for a postural effect on resting state brain activity that persists throughout long-duration HDBR, indicating that immobilization and inactivity per se do not affect resting state electrocortical activity during HDBR. Our findings raise an important issue on the validity of EEG to identify the time course of changes in brain function during prolonged HBDR, and highlight the importance to maintain a consistent body posture during all testing sessions, including data collections at baseline and recovery.

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Section: Discussionmentioning

confidence: 92%

Head-Down Tilt Position, but Not the Duration of Bed Rest Affects Resting State Electrocortical Activity

et al. 2021

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“…EEG's poor spatial resolution is attributable to several factors, including head and/or scalp modeling errors, as well as EEG noise that can limit source localization calculation accuracy ( 52 ). Recently, a mathematical strategy to address this limitation has emerged: Low-Resolution Brain Electromagnetic Tomography (LORETA); see ( 53 , 54 ). LORETA uses signals measured at surface electrodes to infer the distribution of current source density through the full brain volume ( 55 ).…”

Section: Source Localization In Eeg Studies Of Ocd Treatmentmentioning

confidence: 99%

Electroencephalographic Correlates and Predictors of Treatment Outcome in OCD: A Brief Narrative Review

et al. 2021

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Electroencephalography (EEG) measures the brain's electrical activity with high temporal resolution. In comparison to neuroimaging modalities such as MRI or PET, EEG is relatively cheap, non-invasive, portable, and simple to administer, making it an attractive tool for clinical deployment. Despite this, studies utilizing EEG to investigate obsessive-compulsive disorder (OCD) are relatively sparse. This contrasts with a robust literature using other brain imaging methodologies. The present review examines studies that have used EEG to examine predictors and correlates of response in OCD and draws tentative conclusions that may guide much needed future work. Key findings include a limited literature base; few studies have attempted to predict clinical change from EEG signals, and they are confounded by the effects of both pharmacotherapy and psychotherapy. The most robust literature, consisting of several studies, has examined event-related potentials, including the P300, which several studies have reported to be abnormal at baseline in OCD and to normalize with treatment; but even here the literature is quite heterogeneous, and more work is needed. With more robust research, we suggest that the relatively low cost and convenience of EEG, especially in comparison to fMRI and PET, make it well-suited to the development of feasible personalized treatment algorithms.

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“…Then, before testing the resilience indexes, it was necessary to localize the brain active sources. To this aim, the LORETA (Low Resolution Electromagnetic Tomography) framework that is one of the most adopted tools for brain source localization was exploited to reconstruct the sources of brain activity [13]. More precisely, we used the eLORETA (exact LORETA) algorithm and functional connectivity measurements were quantified by the Lagged Linear Connectivity (LLC) parameter (see Section 3.1), implemented in a toolbox recently introduced in the LORETA-KEY software [14,15].…”

Section: Introductionmentioning

confidence: 99%

Testing Graph Robustness Indexes for EEG Analysis in Alzheimer’s Disease Diagnosis

et al. 2021

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Alzheimer’s Disease (AD) is an incurable neurodegenerative disorder which mainly affects older adults. An early diagnosis is essential because medical treatments can slow down the progression of the disease only if provided during the first stage, called Mild Cognitive Impairment (MCI). Starting from the study of electroencephalografic signals, brain functional connectivity analyses can be performed with the support of the graph theory. In particular, the purpose of this work is to verify the performances of three indexes, typically adopted to evaluate the graph robustness, in order to estimate the functional connectivity for three groups of subjects: healthy controls and people affected by dementia at two different stages (MCI and AD). The results obtained by the Connection Density Index, the Randić Index, and a normalized version of the Kirchhoff Index revealed a higher robustness in the brain networks of healthy people, followed by MCI and, finally, by AD patients, consistent with the hallmarks of Alzheimer’s disease. The statistical analysis showed that there is a significant difference between controls and AD for all three indexes. Finally, all three indexes were compared, revealing that the the Randić Index outperformed the other two indexes. These preliminary outcomes will be exploited to address further in-depth and time-expensive analyses for improving the diagnosis of Alzheimer’s disease.

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Findings about LORETA Applied to High-Density EEG—A Review

Cited by 22 publications

References 64 publications

Head-Down Tilt Position, but Not the Duration of Bed Rest Affects Resting State Electrocortical Activity

Head-Down Tilt Position, but Not the Duration of Bed Rest Affects Resting State Electrocortical Activity

Electroencephalographic Correlates and Predictors of Treatment Outcome in OCD: A Brief Narrative Review

Testing Graph Robustness Indexes for EEG Analysis in Alzheimer’s Disease Diagnosis

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